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Center for AI Safety
@CAIS
Reducing societal-scale risks from AI.
加入 August 2022
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Agents sometimes achieve their goals in unintended ways. Recent incidents involving hacks of Hugging Face, DSEWiki and RubyGems illustrate how agents can find creative ways to complete a task while violating the tasks’s expectations. This can happen when reinforcement learning rewards agents for reaching the right outcome without adequately accounting for how they get there. CheatBench proposes a way to measure this reward gaming behavior.
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